# RecipeGen: A Step-Aligned Multimodal Benchmark for Real-World Recipe Generation

arXiv: https://arxiv.org/abs/2506.06733 · HTML: https://mlsanity.com/paper/2506.06733/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 39 references that are in the map.
- First posted on arXiv 7 Jun 2025.

## Builds on (most cited first, 16 of 16)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [HunyuanVideo: A Systematic Framework For Large Video Generative Models](https://mlsanity.com/paper/2412.03603/) (2024)
- [Towards Automatic Learning of Procedures from Web Instructional Videos](https://mlsanity.com/paper/1703.09788/) (2017)
- [In-Context LoRA for Diffusion Transformers](https://mlsanity.com/paper/2410.23775/) (2024)
- [Recipe1M+: A Dataset for Learning Cross-Modal Embeddings for Cooking Recipes and Food Images](https://mlsanity.com/paper/1810.06553/) (2018)
- [A Survey on Food Computing](https://mlsanity.com/paper/1808.07202/) (2018)
- [Visual Goal-Step Inference using wikiHow](https://mlsanity.com/paper/2104.05845/) (2021)
- [RecipeGPT: Generative Pre-training Based Cooking Recipe Generation and Evaluation System](https://mlsanity.com/paper/2003.02498/) (2020)
- [FoodLMM: A Versatile Food Assistant using Large Multi-modal Model](https://mlsanity.com/paper/2312.14991/) (2023)
- [Food Recommendation: Framework, Existing Solutions and Challenges](https://mlsanity.com/paper/1905.06269/) (2019)
- [Structure-Aware Generation Network for Recipe Generation from Images](https://mlsanity.com/paper/2009.00944/) (2020)
- [Learning Structural Representations for Recipe Generation and Food Retrieval](https://mlsanity.com/paper/2110.01209/) (2021)
- [CookGAN: Meal Image Synthesis from Ingredients](https://mlsanity.com/paper/2002.11493/) (2020)
- [FoodSky: A Food-oriented Large Language Model that Passes the Chef and Dietetic Examination](https://mlsanity.com/paper/2406.10261/) (2024)
- [Market2Dish: Health-aware Food Recommendation](https://mlsanity.com/paper/2012.06416/) (2020)

## Abstract (arXiv)

Creating recipe images is a key challenge in food computing, with applications in culinary education and multimodal recipe assistants. However, existing datasets lack fine-grained alignment between recipe goals, step-wise instructions, and visual content. We present RecipeGen, the first large-scale, real-world benchmark for recipe-based Text-to-Image (T2I), Image-to-Video (I2V), and Text-to-Video (T2V) generation. RecipeGen contains 26,453 recipes, 196,724 images, and 4,491 videos, covering diverse ingredients, cooking procedures, styles, and dish types. We further propose domain-specific evaluation metrics to assess ingredient fidelity and interaction modeling, benchmark representative T2I, I2V, and T2V models, and provide insights for future recipe generation models. Project page is available now.